Alternating Current Optimal Power Flow
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Rethinking Neural Width for Alternating Current Optimal Power Flow Proxies
arXiv:2606.03125v1 Announce Type: new Abstract: Deep learning proxies for Alternating Current Optimal Power Flow (ACOPF) lack systematic methods for determining architectural size. This paper conducts a constructive thought experiment to answer a fundamental inquiry: how wide must a neural network be to almost accurately approximate the ACOPF manifold? We introduce a Loss-Guided Neural Densification (LG-ND) algorithm that incrementally discovers necessary capacity by expanding only when the...
Physics-Informed Graph Learning Acceleration for Large-Scale AC-OPF with Topology Changes
arXiv:2606.05772v1 Announce Type: new Abstract: In power systems, alternating current optimal power flow (AC-OPF) has been a challenging problem for decades due to its nonconvexity, but fast and efficient solutions are even more needed because of high penetration of large scale renewable generation and load growth. Recently, neural networks (NN) have gained attention in solving AC-OPF, but it is still in an early stage to be applicable for real and large-scale power system operation with...
Advanced simulation framework for AC/MTDC power systems
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